{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "city='Chengdu'\n",
    "city_cn='成都'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Developer Salary in Nanjing 南京程序员工资调查\n",
    "我在4月1日到3日之间，抓取了某招聘网站的软件和互联网类招聘数据40万条，其中通过程序判断为程序员的14万条。地域方面，我选择了24个主要城市。不过本文只以一线城市为研究对象。这样是为了和我2017年6月的数据做对比。\n",
    "\n",
    "提到2017年的文章，现在居然还有很多人，把这篇文章拿出来炒作。对于社会来说，可气的是，他们直接把2017改成2019，就发表了，这不是骗人么？！对于我来说，可气的是，他们转载居然还冒充是原创，是可忍熟不可忍！"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import sys\n",
    "sys.path.append('..')\n",
    "import db\n",
    "import inspect\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import scipy.stats as stats\n",
    "import numpy as np\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "conn=db.get_conn()\n",
    "data_original=pd.read_sql(sql=\"select * from _51jobs where monthly_salary>0 and monthly_salary<15 and city_{}=1\".format(city), con=conn)\n",
    "conn.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "error_job_ids=['104660258','104142922','108434795','101357291','106253516','110368302','111391233','108665401','109277048'\n",
    "                  ,'73857191','108584955','102824950','102824949','111391233','110884556']\n",
    "data=data_original[~data_original.job_id.isin(error_job_ids)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "del data['publish_date']\n",
    "del data['published_on_weekend']\n",
    "del data['title']\n",
    "del data['company_title']\n",
    "del data['company_description']\n",
    "del data['job_description']\n",
    "del data['job_id']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Unilateral Stats 总体统计"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "有数据可知，程序员向一线城市集中的趋势非常明显。\n",
    "\n",
    "According to the statistics, significant amount of developers are in the first tier cities."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2019年成都程序员的平均工资为11896元，工资中位数为11500元，其中95%的人的工资位于3750到27083元之间。\n"
     ]
    }
   ],
   "source": [
    "salary_mean=data.monthly_salary.mean()*10000\n",
    "salary_median=data.monthly_salary.median()*10000\n",
    "q=np.quantile(data.monthly_salary,[0.025,0.975])*10000\n",
    "print('2019年{}程序员的平均工资为{:.0f}元，工资中位数为{:.0f}元，其中95%的人的工资位于{:.0f}到{:.0f}元之间。'\n",
    "      .format(city_cn,salary_mean, salary_median, q[0], q[1]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "In 2019, Developers in Chengdu earn 11896 Yuan as average, the median is 11500 Yuan, 95% of them earn between 3750 and 27083 Yuan.\n"
     ]
    }
   ],
   "source": [
    "print('In 2019, Developers in {} earn {:.0f} Yuan as average, '\n",
    "      'the median is {:.0f} Yuan, 95% of them earn between {:.0f} and {:.0f} Yuan.'\n",
    "      .format(city,salary_mean, salary_median, q[0], q[1]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x17b2e095470>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data.monthly_salary.hist()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It does not look like normal distribution."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy import stats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "NormaltestResult(statistic=3400.511719984244, pvalue=0.0)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stats.normaltest(data.monthly_salary)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "null hypothesis: x comes from a normal distribution\n",
    "    \n",
    "p=0\n",
    "\n",
    "The null hypothesis can be rejected\n",
    "\n",
    "conclusion: data is not normally distributed."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Zoom in"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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8/kEy4r+joO8gyOed9gBERCJKASAiElEKABGRiFIAiIhElAJARCSiFAAiIhGlABARiSgFgIhIRCkAREQiSgEgIhJRnykAzGy9mZWa2Qozez1o629mC81sbXCdFbSbmd1pZmVmttLMxrTHAoiIyKFpjz2Aie4+2t0Lg/szgWfdfTjwbHAf4BxgeHCZDtzdDvMWEZFD1BGHgCYDc4Pbc4EL4tp/5zGLgX5mltMB8xcRkSR81gBw4C9mtszMpgdt2e5eARBcDw7ac4EP48YtD9pERCQFzN0PfWSzIe6+ycwGAwuB64B57t4vrs82d88ys/nAT9z9paD9WeB77r6s2TSnEztERHZ29tji4uKE866pqSEzM/OQa+9IpRuryM6AzbWpriSxMNcG4a4vvraC3L6pLSaBML8uwlwbhLu+ttY2ceLEZXGH5Vv0mf4PwN03BddbzOxxYByw2cxy3L0iOMSzJeheDgyNGz0P2JRgmrOB2QCFhYVeVFSUcN4lJSW0NCzVps2cz4yCBm4vDeffLYS5Ngh3ffG1rb+sKLXFJBDm10WYa4Nw19dRtR3yISAz62VmvffdBs4CVgHzgKlBt6nAn4Lb84Argk8DjQeq9h0qEhGRzvdZNrOygcfNbN90HnD3P5vZUuBhM7sK2ABcEvRfAJwLlAG7gCs/w7xFROQzOuQAcPd1wAkJ2j8BzkzQ7sA1hzo/ERFpX/omsIhIRCkAREQiSgEgIhJRCgARkYhSAIiIRJQCQEQkohQAIiIRpQAQEYkoBYCISEQpAEREIiqcP7nYTvJnzk91CSIiofU3HQAiHSlVGxjrbz0vJfOVvz06BCQiElEKABGRiFIAiIhElAJARCSiFAAiIhGlABARiSgFgIhIRCkAREQiSgEgIhJRCgARkYjST0F0oLysDO6dehKTfv7Cfu0Xj83jhXc/ZsuOOgC+dWo+DyzZwO76ve0y37lXnsT3Hl3J5uo6zj9hCEP79+SuRWUt9n/0n77IRXe/Ql5WBmMOz2Lem5ua6izI7cvN81YnPe+fXjKKZ9ds4elVH33m5TgUVxcdxcbttWSmd6W2vpHHlm885GkN7p3OD84fydX3L29aF9Xvv9mO1bbNk9dO4Ot3v8yit7dwQ/EbVO9uOKDPjIIG/v225xI+79qDfobib4v2AFLg4rF5ZPfp0XT/WxOGkdEtrV2mnd61C317dmdzdSxczjhmEM+/u+Wg41x09ytALLAmjx7SLnWkymnDB/Hi2kruf23DZ3rzB9iyo46r71/eTpV9NnlZGXxUvZv6RufK+5YmfPMXaSvtAXSwtC7GT75ewNgjsvioajePv7GRgty+/HzKaOrqG/njsnIG9+7Bg9PHs21nPZfes5jVP5zEA0s2cMqRA6iqree6B99g6849TPtiPpedfDgNe52yLTVc9+AbB8zvlCMHsHjdJ033Rwzpw6qN1fTv1Z3/mTKarJ7dYdc2Ljx3MF/7xUts21XP6h9OYuTNz3Dj2V/gqMGZLLh+Ao8u30hVbT3ZfXow98qTOHxAL55Z/RG3Pv02QNM4AOccfxhnHjeYf/3jSgAmHD2QK08dxsDM7twyfw3PvX1gAH19TC7/cNqRAKypqOY7D79Jbr8M/uviURw7oBtjTq7nu398k01Vu/npJaPYXb+XowZlktsvg+8+8iYXjc1jzOFZrPhwW9N8M9O70i3N2LpzD//85eHsrGvknhfXUTx9PLPmr6F0YxVZPbvx5HUTmHDbIn477SRu+/PbvP3RDuZfP4FnVn/Enc+W8Z2vHMPG7bW8XFaZcEt64rGDue5LR3PV3KX823nH7bfHs2+99Oyexj1XFNI3oxtd04zb//IuC9/aTF5WBvddOY6l67c2PSf+4XevU9ewl1F5fbntolHU7mlk6fqtFB07uGneRccM4vl3PwbgpRsnNj12V00Yxt8VDgXgoaUboDq2p9e1i3H7JScwYkgf3q/cyXceXtHiHuaNZx/Ll4/LpmGv8+LaSv7/gjUH7MnFP97yt0MB0MHyB/Tk+gff4KbHSvnlN0/EHUo3VjW9IQFcNWEYl85ezLZd9QD0Su/KqqDP9WcezQ1nDufmeav5p6KjOO22Rexp3EufHrGHriC3L5eNP5yZj5YCcMaxg/jLW5sBGDmkD29VVANww5nDefW9T/hVyXv8z1n9mTw8/4Bab/vz2/zDaUdy1dzXgdieyoicPpx354vsadzLszPOYO4r66mo2n3QZc7LyuAbs1/liP49eXD6eIr+u4S6hk/ffIYPzuSaiUdz8d2vsG1XPX0zugHww8kjeWx5Ofn1H7AhPZ8fnD+S6b9fBkDfjG5ces9ivjIim/+dWsjFd7/KjVtWMu+aCYzIiS3nqUcP5OX3PklYUyJL3t/KSfn9Kd9WS0OjU3hEfwBOyu/PE4+XJhznyLwcTj78KKbdt4Tq2pa3wusa9vKPv19GTV0DWT278fjVp7IweFyaPyfOOT6HJ1Zs5L8vPoGbHitl+YZt3Hj2sftN74xjB/Gjp97ar+343D5cUpjHBXe9jBk8cc2pLFv2MZTv5ajBmXzv0ZUs+2Ab/3XxKC4fn889L647oM6+Gd04a+RhnHn78wBNz6uWTJn9KnOmncRZd7xA+bZaRg7pw39+dQTfmL0YgIX/cjpT5yzhqMGZnHN8Dt9/vBQz+N8rCtle35UJtz3Hr/9+LF/9xUuYQcm/FjH5rpfZHjz3W6JDTx2j0w8BmdnZZvaOmZWZ2czOnn9n+3BbbdOb8KqNVeT1z2h1nMa9zlMrKwB4/I2NnJSfBcDbFdX8fMpoLhidS8NeB2Jhsu/NH6DwiP68vn4rEBz+eSe21XhS/qfH9jdUbGH7rj1J1f/ye5XsqGugrmEvZVtqyO3Xev1PlVbgDus/2cWGrbs4alDmfsO/ePRAni79qCnwqmpj12MOz+JPK2I1Pr58I4X5/ZvG+eua2Jvn2x9VU1mzh3c278Ad3t2yg7ysjLjlPfjhrnhL1m/l5GH9KczPYtE7W+jZPY0e3bqQl5XBusqdB/Q/5agBjB15NN+6b+lB3/wBDPjupGN5+obTuP/bJ3NY3x4MykwHEj8n+vToSq/0NJZv2AbQtB4AuqUZh/XJ4MOttfvN46T8/jyzejO19Y3s2tPIn1d9xJDBAwDYuL2WZR/EphX/HGqupq6BPQ17ue2iUUwaeRi19Y2trrc3P6yifFusltWbqhmQmc7g3ukcl9Obqtp6NlXt5rThgzj9mIEsuH4C86+bwFGDMunXuxfl22rZtmsPI4f04fThg1i9qbrVN3/pOJ26B2BmacBdwFeAcmCpmc1z97cOPubn1564Ld/GvdCjm7V5Gh5cX3nfUk4eNoAvjxjMdWcezVl3vEDjXm/qN7R/BhVVtdQ3xtpOP2YQ/+8PsS1os7bP98D6na5ptl9NAOldm52/8OZ392+wBG2JuH/aZ18d7vvX5O6kdYnVNHpoX/79iQO33BsanaDLfrWuLN9OQV5fNmzdxYtllWT17M6l4w5v2jNrbsPWXZyYk8Gwgb2a+jTsdbrErdtuabFtqgtOzGVAr+587Rcv0bDXeenGiaR367LfskDcc+IgD89J+f15/YOtB7Qf7BGNX3dwwEMSN39n8i9f5otHD+BrJwxh6heP4Jv3vNbicgHs2rN/+C0oreDcghwG9U7nyWDDxQx+teg9HliyoanfjIIGoCsPLf2Qi8fmMSgznYdf//AgSyEdrbMPAY0Dytx9HYCZFQOTgb/ZAEikpq6BzPSu+93vld61aYs4rYtx7vGH8eTKCiaPzmXp+m2YwZB+Gby67hOWrt/K+Sfk0qt72n4nA4uOGdx0nLh3elfSuljT1tXS9Vv56qgcfv38OoYeNoh+Pbu3WtfBVO6o46hBmayrrGHS8dnsrPu0jnMLcnhkeTlDs3pyeP+erPt4/63pl8sq+c3lY7n3pffZHhwCqqqtZ9kH2/jaCUOg4QMuODGX14Mt2GQMH5zJex/vZG+Cd7ry7bsoyO3Lm+VVnFtwWFN7faNTUbWb80blcOdzaxnQqzvfP/e4hIdKADZuq6Vi7Sp+9nfjuPr+5azdUkP5tloKcvsyv7SCs0Zk071r7I2yd4+uVO7cQ8Ne55QjB5CX1fOg9VfXNrCzrpETh/bjjQ+3x9ZD4IxjBlES7MnFe+39rfz0khO4u6QMw5g08jCWL48Ffl5WT8Yc3o/lG7Zz/glDWLr+wAAB6Nk9jYxuaZS88zFvbNjO898tiq2zFpYrkSff3MStF40iq2e3pkNBL7z7MTO+cgxPrNjIrj2NZPdJJyO9C7CXZ1Z/xL985Ri6denC9cUHnsdKpDP+fGdGQQPTQvQvgp1x2KuzAyAXiI/8cuDkTq4h5R5ZVs4tFx5PXX0jF/7qFR5c8iFzrxzHlh11XHrPYnbWNTA8uzdPXnsUO+rqufaBN0gz445vjKZ3j64YxpyX3qd6d8N+5wDOOHYQPwg+sjlh+EBeLqtsmuf//HUtd156Il8dNYT66ko2V+9mZ93+u/tvV+ygYa/z9A2n8ciy8qZDM4nc9ue3mTOtkIqq3bzz0Q56pX+6Zb2usoaHpp/CwMzu/Nvjq6hr2Mvg3uncdtEorrxvKWu31HDXojIemn4Ke91ZvamKf/3jSn745Gr+6+JRHNN/GO98EjsJnKyiYz8Nv3327WXc88I6fvnNMVw4Jo9X3qvcr8+S97dy6tED2V2/lyXvb2VIvwyWvJ/4zRJg+44a/vmhFfzqsjFcNfd1ipds4J4rCnnimlN5payyKQifeGMj9049iXnXnspbFdWUbalpdRlufHQlP/l6AbV7Glm87hN27I6t//FHDuBnC99ttmyxwy+PLCvnT9dMAGIngbOqq4DerN28g4vG5DHrwgLWf7KTPyz+IOE8e6V35Z4rCknv2gUz+HFwnqGl5Upk7ZYaeqWnsbm6jo+Djza/uLaSowdn8tjVXwRgV10jpSteB/ZQ3+gsfu8TqnfXJwxs6TzWfFexQ2dmdgkwyd2/Hdy/HBjn7tfF9ZkOTA/uHgu808LkBgKVLQwLg0Oub+fOnSf26tUruU2jQI8ePWzp0qVfKCgoWANQXFx8xOzZsyufe+65nfuGNzQ0eENDA6effnrub37zm77HHXdcWPe82rzuXn755eGXXnrp+g0bNtQD3HfffUOXL1++684770z+rHAH1ZasPn36dKmurt4LMGvWrMNycnK63XLLLR/99re/zT/jjDPWAqSlpVFZWTk6Ozv7zT179iR68Yb5dTEQqOzSpQurV68ecckll7y3atWqulQXFSf0664N/Y9w90Gt9nL3TrsApwDPxN2/CbjpEKf1emfW3sn11XRATcPd/Q13f7O0tHSnu5+U6nXUgY/tj919ibsPCGFtB7t8w91XuPsqd5/v7oMS9Hnb3W9LUX3tse5GuPs6d7891fV8Dtddu0+3sw8BLQWGm9kwYCMwBfhmJ9fweZDZepc2WwucCFBQUPC6uy/tgHmExX8El8+bh4LLwXzhM0z/cWBYs7Ybgc78gP9bwJGdOD85iE4NAHdvMLNriT3h0oA57p787wyIyGdxYaoLkHDp9C+CufsCYEE7TGp2O0yjI4W5vjDXBuGuL8y1QbjrC3NtEO76OqS2Tj0JLCIi4aEfgxMRiajQB0BrPx1hZulm9lAw/DUzyw9ZfdPM7GMzWxFcvt2Jtc0xsy1mtqqF4WZmdwa1rzSzMSGqrcjMquLW2392Ym1DzWyRma0xs9VmdkOCPqlcd8nUl5L1Z2Y9zGyJmb0Z1PbDBH1S9ppNsr6UvWaD+aeZ2Rtm9lSCYe277lL98aZWPvqUBrxH7FMD3YE3gRHN+lwN/Dq4PQV4KGT1TQN+maL1dzowBljVwvBzgaeJ/arAeOC1ENVWBDyVovWWA4wJbvcG3k3wuKZy3SVTX0rWX7A+MoPb3YDXgPHN+qTyNZtMfSl7zQbz/w7wQKLHr73XXdj3AJp+OsLd9wD7fjoi3mRgbnD7EeBMO9QfvumY+lLG3V8AWv5aa6zW33nMYqCfmeWEpLaUcfcKd18e3N4BrCH2LfZ4qVx3ydSXEsH62Pe1527BpfmJxpS9ZpOsL2XMLA84D/jfFrq067oLewAk+umI5k/0pj7u3gBG4eO2AAACeUlEQVRUAQM6pbrk6gO4KDhM8IiZDe2c0pKSbP2pckqwq/60mY1MRQHBLvaJxLYU44Vi3R2kPkjR+gsOYawAtgAL3b3FdZeC12wy9UHqXrM/B74HtPT3gO267sIeAImSrXlaJ9OnoyQz7yeBfHcfBfyVT9M7DFK57lqznNjX2U8AfgE80dkFmFkm8Cjwz+5e3XxwglE6dd21Ul/K1p+7N7r7aCAPGGdmxzfrktJ1l0R9KXnNmtlXgS3uvuxg3RK0HfK6C3sAlAPx6ZsHbGqpj5l1BfrSeYcWWq3P3T9x932/d3IPMLaTaktGMus3Jdy9et+uuse+O9LNzAZ21vzNrBuxN9f73f2xBF1Suu5aqy/V6y+Y73agBDi72aBUvmabtFRfCl+zpwLnm9l6YoeTv2Rmf2jWp13XXdgDoOmnI8ysO7GTHvOa9ZkHTA1uXww858EZkjDU1+y48PnEjteGxTzgiuATLeOBKnevSHVRAGZ22L5jm2Y2jthztb1/2K2leRtwL7DG3X/WQreUrbtk6kvV+jOzQWbWL7idAXwZeLtZt5S9ZpOpL1WvWXe/yd3z3D2f2HvJc+7+9826teu6C/VfQnoLPx1hZj8i9uNI84i9EH5vZmXEknBKyOq73szOBxqC+qZ1Vn1m9iCxT4MMNLNy4GZiJ71w918T+0b2uUAZsAu4MkS1XQz8k5k1ALXAlE4M9lOBy4HS4FgxwPeBw+PqS9m6S7K+VK2/HGCuxf78qQvwsLs/FZbXbJL1pew1m0hHrjt9E1hEJKLCfghIREQ6iAJARCSiFAAiIhGlABARiSgFgIhIRCkAREQiSgEgIhJRCgARkYj6PwtMLU3XytEAAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data[data.monthly_salary<4].monthly_salary.hist()\n",
    "plt.annotate('https://github.com/juwikuang/job_survey', xy=(0,0), xytext=(0.5, 20), color='white')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Role 角色"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_sub_stats(data, prefix):\n",
    "    \n",
    "    features = [feature for feature in data.columns if feature.startswith(prefix)]\n",
    "    salary_mean=[]\n",
    "    salary_median=[]\n",
    "    count=[]\n",
    "    for feature in features:\n",
    "        salary_mean.append(data[data[feature]==1].monthly_salary.mean()*10000)\n",
    "        salary_median.append(data[data[feature]==1].monthly_salary.median()*10000)\n",
    "        count.append(data[data[feature]==1].shape[0])\n",
    "    sub_data=pd.DataFrame()\n",
    "    sub_data[prefix]=[f.replace(prefix,'') for f in features]\n",
    "    sub_data['salary_mean']=salary_mean\n",
    "    sub_data['salary_median']=salary_median\n",
    "    sub_data['head_count']=count\n",
    "    sub_data['percentage']=count/np.sum(count)\n",
    "    sub_data=sub_data.sort_values(by='salary_mean', ascending=False)\n",
    "\n",
    "    return sub_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "def add_category_column(data, prefix):\n",
    "    prefix_=prefix+\"_\"\n",
    "    features = [feature for feature in data.columns if feature.startswith(prefix)]\n",
    "    data.loc[:,prefix]=['']*data.shape[0]\n",
    "    for feature in features:\n",
    "        data.loc[data[feature]==1,prefix]=feature.replace(prefix_,'')\n",
    "    return data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style  type=\"text/css\" >\n",
       "</style>  \n",
       "<table id=\"T_626ce88c_584a_11e9_88d5_701ce71031ef\" > \n",
       "<thead>    <tr> \n",
       "        <th class=\"blank level0\" ></th> \n",
       "        <th class=\"col_heading level0 col0\" >career_</th> \n",
       "        <th class=\"col_heading level0 col1\" >salary_mean</th> \n",
       "        <th class=\"col_heading level0 col2\" >salary_median</th> \n",
       "        <th class=\"col_heading level0 col3\" >head_count</th> \n",
       "        <th class=\"col_heading level0 col4\" >percentage</th> \n",
       "    </tr></thead> \n",
       "<tbody>    <tr> \n",
       "        <th id=\"T_626ce88c_584a_11e9_88d5_701ce71031eflevel0_row0\" class=\"row_heading level0 row0\" >1</th> \n",
       "        <td id=\"T_626ce88c_584a_11e9_88d5_701ce71031efrow0_col0\" class=\"data row0 col0\" >architect</td> \n",
       "        <td id=\"T_626ce88c_584a_11e9_88d5_701ce71031efrow0_col1\" class=\"data row0 col1\" >18350.3</td> \n",
       "        <td id=\"T_626ce88c_584a_11e9_88d5_701ce71031efrow0_col2\" class=\"data row0 col2\" >17500</td> \n",
       "        <td id=\"T_626ce88c_584a_11e9_88d5_701ce71031efrow0_col3\" class=\"data row0 col3\" >291</td> \n",
       "        <td id=\"T_626ce88c_584a_11e9_88d5_701ce71031efrow0_col4\" class=\"data row0 col4\" >4.05%</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_626ce88c_584a_11e9_88d5_701ce71031eflevel0_row1\" class=\"row_heading level0 row1\" >0</th> \n",
       "        <td id=\"T_626ce88c_584a_11e9_88d5_701ce71031efrow1_col0\" class=\"data row1 col0\" >algorithm</td> \n",
       "        <td id=\"T_626ce88c_584a_11e9_88d5_701ce71031efrow1_col1\" class=\"data row1 col1\" >17406.2</td> \n",
       "        <td id=\"T_626ce88c_584a_11e9_88d5_701ce71031efrow1_col2\" class=\"data row1 col2\" >17083.3</td> \n",
       "        <td id=\"T_626ce88c_584a_11e9_88d5_701ce71031efrow1_col3\" class=\"data row1 col3\" >758</td> \n",
       "        <td id=\"T_626ce88c_584a_11e9_88d5_701ce71031efrow1_col4\" class=\"data row1 col4\" >10.54%</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_626ce88c_584a_11e9_88d5_701ce71031eflevel0_row2\" class=\"row_heading level0 row2\" >2</th> \n",
       "        <td id=\"T_626ce88c_584a_11e9_88d5_701ce71031efrow2_col0\" class=\"data row2 col0\" >software_engineer</td> \n",
       "        <td id=\"T_626ce88c_584a_11e9_88d5_701ce71031efrow2_col1\" class=\"data row2 col1\" >11178.7</td> \n",
       "        <td id=\"T_626ce88c_584a_11e9_88d5_701ce71031efrow2_col2\" class=\"data row2 col2\" >10500</td> \n",
       "        <td id=\"T_626ce88c_584a_11e9_88d5_701ce71031efrow2_col3\" class=\"data row2 col3\" >6141</td> \n",
       "        <td id=\"T_626ce88c_584a_11e9_88d5_701ce71031efrow2_col4\" class=\"data row2 col4\" >85.41%</td> \n",
       "    </tr></tbody> \n",
       "</table> "
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x17b2c883128>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_career=get_sub_stats(data,'career_')\n",
    "data_career.style.format({\"percentage\":\"{:.2%}\"})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 编程语言"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style  type=\"text/css\" >\n",
       "</style>  \n",
       "<table id=\"T_63743da8_584a_11e9_9512_701ce71031ef\" > \n",
       "<thead>    <tr> \n",
       "        <th class=\"blank level0\" ></th> \n",
       "        <th class=\"col_heading level0 col0\" >pl_</th> \n",
       "        <th class=\"col_heading level0 col1\" >salary_mean</th> \n",
       "        <th class=\"col_heading level0 col2\" >salary_median</th> \n",
       "        <th class=\"col_heading level0 col3\" >head_count</th> \n",
       "        <th class=\"col_heading level0 col4\" >percentage</th> \n",
       "    </tr></thead> \n",
       "<tbody>    <tr> \n",
       "        <th id=\"T_63743da8_584a_11e9_9512_701ce71031eflevel0_row0\" class=\"row_heading level0 row0\" >6</th> \n",
       "        <td id=\"T_63743da8_584a_11e9_9512_701ce71031efrow0_col0\" class=\"data row0 col0\" >haskell</td> \n",
       "        <td id=\"T_63743da8_584a_11e9_9512_701ce71031efrow0_col1\" class=\"data row0 col1\" >31666.7</td> \n",
       "        <td id=\"T_63743da8_584a_11e9_9512_701ce71031efrow0_col2\" class=\"data row0 col2\" >31666.7</td> \n",
       "        <td id=\"T_63743da8_584a_11e9_9512_701ce71031efrow0_col3\" class=\"data row0 col3\" >1</td> \n",
       "        <td id=\"T_63743da8_584a_11e9_9512_701ce71031efrow0_col4\" class=\"data row0 col4\" >0.01%</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_63743da8_584a_11e9_9512_701ce71031eflevel0_row1\" class=\"row_heading level0 row1\" >0</th> \n",
       "        <td id=\"T_63743da8_584a_11e9_9512_701ce71031efrow1_col0\" class=\"data row1 col0\" >assembly</td> \n",
       "        <td id=\"T_63743da8_584a_11e9_9512_701ce71031efrow1_col1\" class=\"data row1 col1\" >17604.9</td> \n",
       "        <td id=\"T_63743da8_584a_11e9_9512_701ce71031efrow1_col2\" class=\"data row1 col2\" >15000</td> \n",
       "        <td id=\"T_63743da8_584a_11e9_9512_701ce71031efrow1_col3\" class=\"data row1 col3\" >54</td> \n",
       "        <td id=\"T_63743da8_584a_11e9_9512_701ce71031efrow1_col4\" class=\"data row1 col4\" >0.57%</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_63743da8_584a_11e9_9512_701ce71031eflevel0_row2\" class=\"row_heading level0 row2\" >18</th> \n",
       "        <td id=\"T_63743da8_584a_11e9_9512_701ce71031efrow2_col0\" class=\"data row2 col0\" >rust</td> \n",
       "        <td id=\"T_63743da8_584a_11e9_9512_701ce71031efrow2_col1\" class=\"data row2 col1\" >17128.2</td> \n",
       "        <td id=\"T_63743da8_584a_11e9_9512_701ce71031efrow2_col2\" class=\"data row2 col2\" >17500</td> \n",
       "        <td id=\"T_63743da8_584a_11e9_9512_701ce71031efrow2_col3\" class=\"data row2 col3\" >13</td> \n",
       "        <td id=\"T_63743da8_584a_11e9_9512_701ce71031efrow2_col4\" class=\"data row2 col4\" >0.14%</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_63743da8_584a_11e9_9512_701ce71031eflevel0_row3\" class=\"row_heading level0 row3\" >19</th> \n",
       "        <td id=\"T_63743da8_584a_11e9_9512_701ce71031efrow3_col0\" class=\"data row3 col0\" >scala</td> \n",
       "        <td id=\"T_63743da8_584a_11e9_9512_701ce71031efrow3_col1\" class=\"data row3 col1\" >17106.8</td> \n",
       "        <td id=\"T_63743da8_584a_11e9_9512_701ce71031efrow3_col2\" class=\"data row3 col2\" >16500</td> \n",
       "        <td id=\"T_63743da8_584a_11e9_9512_701ce71031efrow3_col3\" class=\"data row3 col3\" >71</td> \n",
       "        <td id=\"T_63743da8_584a_11e9_9512_701ce71031efrow3_col4\" class=\"data row3 col4\" >0.74%</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_63743da8_584a_11e9_9512_701ce71031eflevel0_row4\" class=\"row_heading level0 row4\" >25</th> \n",
       "        <td id=\"T_63743da8_584a_11e9_9512_701ce71031efrow4_col0\" class=\"data row4 col0\" >r</td> \n",
       "        <td id=\"T_63743da8_584a_11e9_9512_701ce71031efrow4_col1\" class=\"data row4 col1\" >16596.8</td> \n",
       "        <td id=\"T_63743da8_584a_11e9_9512_701ce71031efrow4_col2\" class=\"data row4 col2\" >12500</td> \n",
       "        <td id=\"T_63743da8_584a_11e9_9512_701ce71031efrow4_col3\" class=\"data row4 col3\" >31</td> \n",
       "        <td id=\"T_63743da8_584a_11e9_9512_701ce71031efrow4_col4\" class=\"data row4 col4\" >0.32%</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_63743da8_584a_11e9_9512_701ce71031eflevel0_row5\" class=\"row_heading level0 row5\" >17</th> \n",
       "        <td id=\"T_63743da8_584a_11e9_9512_701ce71031efrow5_col0\" class=\"data row5 col0\" >ruby</td> \n",
       "        <td id=\"T_63743da8_584a_11e9_9512_701ce71031efrow5_col1\" class=\"data row5 col1\" >16247.4</td> \n",
       "        <td id=\"T_63743da8_584a_11e9_9512_701ce71031efrow5_col2\" class=\"data row5 col2\" >14500</td> \n",
       "        <td id=\"T_63743da8_584a_11e9_9512_701ce71031efrow5_col3\" class=\"data row5 col3\" >32</td> \n",
       "        <td id=\"T_63743da8_584a_11e9_9512_701ce71031efrow5_col4\" class=\"data row5 col4\" >0.33%</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_63743da8_584a_11e9_9512_701ce71031eflevel0_row6\" class=\"row_heading level0 row6\" >12</th> \n",
       "        <td id=\"T_63743da8_584a_11e9_9512_701ce71031efrow6_col0\" class=\"data row6 col0\" >matlab</td> \n",
       "        <td id=\"T_63743da8_584a_11e9_9512_701ce71031efrow6_col1\" class=\"data row6 col1\" >16151</td> \n",
       "        <td id=\"T_63743da8_584a_11e9_9512_701ce71031efrow6_col2\" class=\"data row6 col2\" >15000</td> \n",
       "        <td id=\"T_63743da8_584a_11e9_9512_701ce71031efrow6_col3\" class=\"data row6 col3\" >181</td> \n",
       "        <td id=\"T_63743da8_584a_11e9_9512_701ce71031efrow6_col4\" class=\"data row6 col4\" >1.89%</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_63743da8_584a_11e9_9512_701ce71031eflevel0_row7\" class=\"row_heading level0 row7\" >14</th> \n",
       "        <td id=\"T_63743da8_584a_11e9_9512_701ce71031efrow7_col0\" class=\"data row7 col0\" >perl</td> \n",
       "        <td id=\"T_63743da8_584a_11e9_9512_701ce71031efrow7_col1\" class=\"data row7 col1\" >15002.8</td> \n",
       "        <td id=\"T_63743da8_584a_11e9_9512_701ce71031efrow7_col2\" class=\"data row7 col2\" >12500</td> \n",
       "        <td id=\"T_63743da8_584a_11e9_9512_701ce71031efrow7_col3\" class=\"data row7 col3\" >65</td> \n",
       "        <td id=\"T_63743da8_584a_11e9_9512_701ce71031efrow7_col4\" class=\"data row7 col4\" >0.68%</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_63743da8_584a_11e9_9512_701ce71031eflevel0_row8\" class=\"row_heading level0 row8\" >16</th> \n",
       "        <td id=\"T_63743da8_584a_11e9_9512_701ce71031efrow8_col0\" class=\"data row8 col0\" >python</td> \n",
       "        <td id=\"T_63743da8_584a_11e9_9512_701ce71031efrow8_col1\" class=\"data row8 col1\" >14921.7</td> \n",
       "        <td id=\"T_63743da8_584a_11e9_9512_701ce71031efrow8_col2\" class=\"data row8 col2\" >12500</td> \n",
       "        <td id=\"T_63743da8_584a_11e9_9512_701ce71031efrow8_col3\" class=\"data row8 col3\" >784</td> \n",
       "        <td id=\"T_63743da8_584a_11e9_9512_701ce71031efrow8_col4\" class=\"data row8 col4\" >8.21%</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_63743da8_584a_11e9_9512_701ce71031eflevel0_row9\" class=\"row_heading level0 row9\" >4</th> \n",
       "        <td id=\"T_63743da8_584a_11e9_9512_701ce71031efrow9_col0\" class=\"data row9 col0\" >go</td> \n",
       "        <td id=\"T_63743da8_584a_11e9_9512_701ce71031efrow9_col1\" class=\"data row9 col1\" >14565.7</td> \n",
       "        <td id=\"T_63743da8_584a_11e9_9512_701ce71031efrow9_col2\" class=\"data row9 col2\" >12500</td> \n",
       "        <td id=\"T_63743da8_584a_11e9_9512_701ce71031efrow9_col3\" class=\"data row9 col3\" >755</td> \n",
       "        <td id=\"T_63743da8_584a_11e9_9512_701ce71031efrow9_col4\" class=\"data row9 col4\" >7.90%</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_63743da8_584a_11e9_9512_701ce71031eflevel0_row10\" class=\"row_heading level0 row10\" >24</th> \n",
       "        <td id=\"T_63743da8_584a_11e9_9512_701ce71031efrow10_col0\" class=\"data row10 col0\" >visual_basic_net</td> \n",
       "        <td id=\"T_63743da8_584a_11e9_9512_701ce71031efrow10_col1\" class=\"data row10 col1\" >14166.7</td> \n",
       "        <td id=\"T_63743da8_584a_11e9_9512_701ce71031efrow10_col2\" class=\"data row10 col2\" >13750</td> \n",
       "        <td id=\"T_63743da8_584a_11e9_9512_701ce71031efrow10_col3\" class=\"data row10 col3\" >12</td> \n",
       "        <td id=\"T_63743da8_584a_11e9_9512_701ce71031efrow10_col4\" class=\"data row10 col4\" >0.13%</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_63743da8_584a_11e9_9512_701ce71031eflevel0_row11\" class=\"row_heading level0 row11\" >5</th> \n",
       "        <td id=\"T_63743da8_584a_11e9_9512_701ce71031efrow11_col0\" class=\"data row11 col0\" >groovy</td> \n",
       "        <td id=\"T_63743da8_584a_11e9_9512_701ce71031efrow11_col1\" class=\"data row11 col1\" >14166.7</td> \n",
       "        <td id=\"T_63743da8_584a_11e9_9512_701ce71031efrow11_col2\" class=\"data row11 col2\" >15000</td> \n",
       "        <td id=\"T_63743da8_584a_11e9_9512_701ce71031efrow11_col3\" class=\"data row11 col3\" >3</td> \n",
       "        <td id=\"T_63743da8_584a_11e9_9512_701ce71031efrow11_col4\" class=\"data row11 col4\" >0.03%</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_63743da8_584a_11e9_9512_701ce71031eflevel0_row12\" class=\"row_heading level0 row12\" >9</th> \n",
       "        <td id=\"T_63743da8_584a_11e9_9512_701ce71031efrow12_col0\" class=\"data row12 col0\" >julia</td> \n",
       "        <td id=\"T_63743da8_584a_11e9_9512_701ce71031efrow12_col1\" class=\"data row12 col1\" >14000</td> \n",
       "        <td id=\"T_63743da8_584a_11e9_9512_701ce71031efrow12_col2\" class=\"data row12 col2\" >14000</td> \n",
       "        <td id=\"T_63743da8_584a_11e9_9512_701ce71031efrow12_col3\" class=\"data row12 col3\" >1</td> \n",
       "        <td id=\"T_63743da8_584a_11e9_9512_701ce71031efrow12_col4\" class=\"data row12 col4\" >0.01%</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_63743da8_584a_11e9_9512_701ce71031eflevel0_row13\" class=\"row_heading level0 row13\" >11</th> \n",
       "        <td id=\"T_63743da8_584a_11e9_9512_701ce71031efrow13_col0\" class=\"data row13 col0\" >lua</td> \n",
       "        <td id=\"T_63743da8_584a_11e9_9512_701ce71031efrow13_col1\" class=\"data row13 col1\" >13956.1</td> \n",
       "        <td id=\"T_63743da8_584a_11e9_9512_701ce71031efrow13_col2\" class=\"data row13 col2\" >12500</td> \n",
       "        <td id=\"T_63743da8_584a_11e9_9512_701ce71031efrow13_col3\" class=\"data row13 col3\" >177</td> \n",
       "        <td id=\"T_63743da8_584a_11e9_9512_701ce71031efrow13_col4\" class=\"data row13 col4\" >1.85%</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_63743da8_584a_11e9_9512_701ce71031eflevel0_row14\" class=\"row_heading level0 row14\" >2</th> \n",
       "        <td id=\"T_63743da8_584a_11e9_9512_701ce71031efrow14_col0\" class=\"data row14 col0\" >cpp</td> \n",
       "        <td id=\"T_63743da8_584a_11e9_9512_701ce71031efrow14_col1\" class=\"data row14 col1\" >13597.6</td> \n",
       "        <td id=\"T_63743da8_584a_11e9_9512_701ce71031efrow14_col2\" class=\"data row14 col2\" >12500</td> \n",
       "        <td id=\"T_63743da8_584a_11e9_9512_701ce71031efrow14_col3\" class=\"data row14 col3\" >1689</td> \n",
       "        <td id=\"T_63743da8_584a_11e9_9512_701ce71031efrow14_col4\" class=\"data row14 col4\" >17.68%</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_63743da8_584a_11e9_9512_701ce71031eflevel0_row15\" class=\"row_heading level0 row15\" >7</th> \n",
       "        <td id=\"T_63743da8_584a_11e9_9512_701ce71031efrow15_col0\" class=\"data row15 col0\" >java</td> \n",
       "        <td id=\"T_63743da8_584a_11e9_9512_701ce71031efrow15_col1\" class=\"data row15 col1\" >12200.3</td> \n",
       "        <td id=\"T_63743da8_584a_11e9_9512_701ce71031efrow15_col2\" class=\"data row15 col2\" >11500</td> \n",
       "        <td id=\"T_63743da8_584a_11e9_9512_701ce71031efrow15_col3\" class=\"data row15 col3\" >2561</td> \n",
       "        <td id=\"T_63743da8_584a_11e9_9512_701ce71031efrow15_col4\" class=\"data row15 col4\" >26.80%</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_63743da8_584a_11e9_9512_701ce71031eflevel0_row16\" class=\"row_heading level0 row16\" >22</th> \n",
       "        <td id=\"T_63743da8_584a_11e9_9512_701ce71031efrow16_col0\" class=\"data row16 col0\" >vba</td> \n",
       "        <td id=\"T_63743da8_584a_11e9_9512_701ce71031efrow16_col1\" class=\"data row16 col1\" >12125</td> \n",
       "        <td id=\"T_63743da8_584a_11e9_9512_701ce71031efrow16_col2\" class=\"data row16 col2\" >9000</td> \n",
       "        <td id=\"T_63743da8_584a_11e9_9512_701ce71031efrow16_col3\" class=\"data row16 col3\" >8</td> \n",
       "        <td id=\"T_63743da8_584a_11e9_9512_701ce71031efrow16_col4\" class=\"data row16 col4\" >0.08%</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_63743da8_584a_11e9_9512_701ce71031eflevel0_row17\" class=\"row_heading level0 row17\" >21</th> \n",
       "        <td id=\"T_63743da8_584a_11e9_9512_701ce71031efrow17_col0\" class=\"data row17 col0\" >typescript</td> \n",
       "        <td id=\"T_63743da8_584a_11e9_9512_701ce71031efrow17_col1\" class=\"data row17 col1\" >11675.6</td> \n",
       "        <td id=\"T_63743da8_584a_11e9_9512_701ce71031efrow17_col2\" class=\"data row17 col2\" >11000</td> \n",
       "        <td id=\"T_63743da8_584a_11e9_9512_701ce71031efrow17_col3\" class=\"data row17 col3\" >65</td> \n",
       "        <td id=\"T_63743da8_584a_11e9_9512_701ce71031efrow17_col4\" class=\"data row17 col4\" >0.68%</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_63743da8_584a_11e9_9512_701ce71031eflevel0_row18\" class=\"row_heading level0 row18\" >13</th> \n",
       "        <td id=\"T_63743da8_584a_11e9_9512_701ce71031efrow18_col0\" class=\"data row18 col0\" >objective_c</td> \n",
       "        <td id=\"T_63743da8_584a_11e9_9512_701ce71031efrow18_col1\" class=\"data row18 col1\" >11080</td> \n",
       "        <td id=\"T_63743da8_584a_11e9_9512_701ce71031efrow18_col2\" class=\"data row18 col2\" >10500</td> \n",
       "        <td id=\"T_63743da8_584a_11e9_9512_701ce71031efrow18_col3\" class=\"data row18 col3\" >5</td> \n",
       "        <td id=\"T_63743da8_584a_11e9_9512_701ce71031efrow18_col4\" class=\"data row18 col4\" >0.05%</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_63743da8_584a_11e9_9512_701ce71031eflevel0_row19\" class=\"row_heading level0 row19\" >15</th> \n",
       "        <td id=\"T_63743da8_584a_11e9_9512_701ce71031efrow19_col0\" class=\"data row19 col0\" >php</td> \n",
       "        <td id=\"T_63743da8_584a_11e9_9512_701ce71031efrow19_col1\" class=\"data row19 col1\" >10937.3</td> \n",
       "        <td id=\"T_63743da8_584a_11e9_9512_701ce71031efrow19_col2\" class=\"data row19 col2\" >10000</td> \n",
       "        <td id=\"T_63743da8_584a_11e9_9512_701ce71031efrow19_col3\" class=\"data row19 col3\" >408</td> \n",
       "        <td id=\"T_63743da8_584a_11e9_9512_701ce71031efrow19_col4\" class=\"data row19 col4\" >4.27%</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_63743da8_584a_11e9_9512_701ce71031eflevel0_row20\" class=\"row_heading level0 row20\" >3</th> \n",
       "        <td id=\"T_63743da8_584a_11e9_9512_701ce71031efrow20_col0\" class=\"data row20 col0\" >delphi</td> \n",
       "        <td id=\"T_63743da8_584a_11e9_9512_701ce71031efrow20_col1\" class=\"data row20 col1\" >10805.6</td> \n",
       "        <td id=\"T_63743da8_584a_11e9_9512_701ce71031efrow20_col2\" class=\"data row20 col2\" >11375</td> \n",
       "        <td id=\"T_63743da8_584a_11e9_9512_701ce71031efrow20_col3\" class=\"data row20 col3\" >24</td> \n",
       "        <td id=\"T_63743da8_584a_11e9_9512_701ce71031efrow20_col4\" class=\"data row20 col4\" >0.25%</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_63743da8_584a_11e9_9512_701ce71031eflevel0_row21\" class=\"row_heading level0 row21\" >20</th> \n",
       "        <td id=\"T_63743da8_584a_11e9_9512_701ce71031efrow21_col0\" class=\"data row21 col0\" >swift</td> \n",
       "        <td id=\"T_63743da8_584a_11e9_9512_701ce71031efrow21_col1\" class=\"data row21 col1\" >10654.5</td> \n",
       "        <td id=\"T_63743da8_584a_11e9_9512_701ce71031efrow21_col2\" class=\"data row21 col2\" >10000</td> \n",
       "        <td id=\"T_63743da8_584a_11e9_9512_701ce71031efrow21_col3\" class=\"data row21 col3\" >89</td> \n",
       "        <td id=\"T_63743da8_584a_11e9_9512_701ce71031efrow21_col4\" class=\"data row21 col4\" >0.93%</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_63743da8_584a_11e9_9512_701ce71031eflevel0_row22\" class=\"row_heading level0 row22\" >1</th> \n",
       "        <td id=\"T_63743da8_584a_11e9_9512_701ce71031efrow22_col0\" class=\"data row22 col0\" >c_sharp</td> \n",
       "        <td id=\"T_63743da8_584a_11e9_9512_701ce71031efrow22_col1\" class=\"data row22 col1\" >10473.3</td> \n",
       "        <td id=\"T_63743da8_584a_11e9_9512_701ce71031efrow22_col2\" class=\"data row22 col2\" >9500</td> \n",
       "        <td id=\"T_63743da8_584a_11e9_9512_701ce71031efrow22_col3\" class=\"data row22 col3\" >1073</td> \n",
       "        <td id=\"T_63743da8_584a_11e9_9512_701ce71031efrow22_col4\" class=\"data row22 col4\" >11.23%</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_63743da8_584a_11e9_9512_701ce71031eflevel0_row23\" class=\"row_heading level0 row23\" >8</th> \n",
       "        <td id=\"T_63743da8_584a_11e9_9512_701ce71031efrow23_col0\" class=\"data row23 col0\" >javascript</td> \n",
       "        <td id=\"T_63743da8_584a_11e9_9512_701ce71031efrow23_col1\" class=\"data row23 col1\" >10306.6</td> \n",
       "        <td id=\"T_63743da8_584a_11e9_9512_701ce71031efrow23_col2\" class=\"data row23 col2\" >9500</td> \n",
       "        <td id=\"T_63743da8_584a_11e9_9512_701ce71031efrow23_col3\" class=\"data row23 col3\" >1419</td> \n",
       "        <td id=\"T_63743da8_584a_11e9_9512_701ce71031efrow23_col4\" class=\"data row23 col4\" >14.85%</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_63743da8_584a_11e9_9512_701ce71031eflevel0_row24\" class=\"row_heading level0 row24\" >10</th> \n",
       "        <td id=\"T_63743da8_584a_11e9_9512_701ce71031efrow24_col0\" class=\"data row24 col0\" >kotlin</td> \n",
       "        <td id=\"T_63743da8_584a_11e9_9512_701ce71031efrow24_col1\" class=\"data row24 col1\" >9970.59</td> \n",
       "        <td id=\"T_63743da8_584a_11e9_9512_701ce71031efrow24_col2\" class=\"data row24 col2\" >10000</td> \n",
       "        <td id=\"T_63743da8_584a_11e9_9512_701ce71031efrow24_col3\" class=\"data row24 col3\" >34</td> \n",
       "        <td id=\"T_63743da8_584a_11e9_9512_701ce71031efrow24_col4\" class=\"data row24 col4\" >0.36%</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_63743da8_584a_11e9_9512_701ce71031eflevel0_row25\" class=\"row_heading level0 row25\" >23</th> \n",
       "        <td id=\"T_63743da8_584a_11e9_9512_701ce71031efrow25_col0\" class=\"data row25 col0\" >visual_basic</td> \n",
       "        <td id=\"T_63743da8_584a_11e9_9512_701ce71031efrow25_col1\" class=\"data row25 col1\" >nan</td> \n",
       "        <td id=\"T_63743da8_584a_11e9_9512_701ce71031efrow25_col2\" class=\"data row25 col2\" >nan</td> \n",
       "        <td id=\"T_63743da8_584a_11e9_9512_701ce71031efrow25_col3\" class=\"data row25 col3\" >0</td> \n",
       "        <td id=\"T_63743da8_584a_11e9_9512_701ce71031efrow25_col4\" class=\"data row25 col4\" >0.00%</td> \n",
       "    </tr></tbody> \n",
       "</table> "
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x17b2df559b0>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_pl=get_sub_stats(data,'pl_')\n",
    "data_pl.style.format({\"percentage\":\"{:.2%}\"})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 教育"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style  type=\"text/css\" >\n",
       "</style>  \n",
       "<table id=\"T_73da9efa_584a_11e9_81fa_701ce71031ef\" > \n",
       "<thead>    <tr> \n",
       "        <th class=\"blank level0\" ></th> \n",
       "        <th class=\"col_heading level0 col0\" >edu_</th> \n",
       "        <th class=\"col_heading level0 col1\" >salary_mean</th> \n",
       "        <th class=\"col_heading level0 col2\" >salary_median</th> \n",
       "        <th class=\"col_heading level0 col3\" >head_count</th> \n",
       "        <th class=\"col_heading level0 col4\" >percentage</th> \n",
       "    </tr></thead> \n",
       "<tbody>    <tr> \n",
       "        <th id=\"T_73da9efa_584a_11e9_81fa_701ce71031eflevel0_row0\" class=\"row_heading level0 row0\" >5</th> \n",
       "        <td id=\"T_73da9efa_584a_11e9_81fa_701ce71031efrow0_col0\" class=\"data row0 col0\" >phd</td> \n",
       "        <td id=\"T_73da9efa_584a_11e9_81fa_701ce71031efrow0_col1\" class=\"data row0 col1\" >23750</td> \n",
       "        <td id=\"T_73da9efa_584a_11e9_81fa_701ce71031efrow0_col2\" class=\"data row0 col2\" >22500</td> \n",
       "        <td id=\"T_73da9efa_584a_11e9_81fa_701ce71031efrow0_col3\" class=\"data row0 col3\" >6</td> \n",
       "        <td id=\"T_73da9efa_584a_11e9_81fa_701ce71031efrow0_col4\" class=\"data row0 col4\" >0.10%</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_73da9efa_584a_11e9_81fa_701ce71031eflevel0_row1\" class=\"row_heading level0 row1\" >3</th> \n",
       "        <td id=\"T_73da9efa_584a_11e9_81fa_701ce71031efrow1_col0\" class=\"data row1 col0\" >master</td> \n",
       "        <td id=\"T_73da9efa_584a_11e9_81fa_701ce71031efrow1_col1\" class=\"data row1 col1\" >17568.8</td> \n",
       "        <td id=\"T_73da9efa_584a_11e9_81fa_701ce71031efrow1_col2\" class=\"data row1 col2\" >17500</td> \n",
       "        <td id=\"T_73da9efa_584a_11e9_81fa_701ce71031efrow1_col3\" class=\"data row1 col3\" >195</td> \n",
       "        <td id=\"T_73da9efa_584a_11e9_81fa_701ce71031efrow1_col4\" class=\"data row1 col4\" >3.37%</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_73da9efa_584a_11e9_81fa_701ce71031eflevel0_row2\" class=\"row_heading level0 row2\" >1</th> \n",
       "        <td id=\"T_73da9efa_584a_11e9_81fa_701ce71031efrow2_col0\" class=\"data row2 col0\" >bachelor</td> \n",
       "        <td id=\"T_73da9efa_584a_11e9_81fa_701ce71031efrow2_col1\" class=\"data row2 col1\" >12802.7</td> \n",
       "        <td id=\"T_73da9efa_584a_11e9_81fa_701ce71031efrow2_col2\" class=\"data row2 col2\" >12000</td> \n",
       "        <td id=\"T_73da9efa_584a_11e9_81fa_701ce71031efrow2_col3\" class=\"data row2 col3\" >3469</td> \n",
       "        <td id=\"T_73da9efa_584a_11e9_81fa_701ce71031efrow2_col4\" class=\"data row2 col4\" >59.99%</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_73da9efa_584a_11e9_81fa_701ce71031eflevel0_row3\" class=\"row_heading level0 row3\" >4</th> \n",
       "        <td id=\"T_73da9efa_584a_11e9_81fa_701ce71031efrow3_col0\" class=\"data row3 col0\" >middle_school</td> \n",
       "        <td id=\"T_73da9efa_584a_11e9_81fa_701ce71031efrow3_col1\" class=\"data row3 col1\" >10000</td> \n",
       "        <td id=\"T_73da9efa_584a_11e9_81fa_701ce71031efrow3_col2\" class=\"data row3 col2\" >10000</td> \n",
       "        <td id=\"T_73da9efa_584a_11e9_81fa_701ce71031efrow3_col3\" class=\"data row3 col3\" >2</td> \n",
       "        <td id=\"T_73da9efa_584a_11e9_81fa_701ce71031efrow3_col4\" class=\"data row3 col4\" >0.03%</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_73da9efa_584a_11e9_81fa_701ce71031eflevel0_row4\" class=\"row_heading level0 row4\" >0</th> \n",
       "        <td id=\"T_73da9efa_584a_11e9_81fa_701ce71031efrow4_col0\" class=\"data row4 col0\" >associate</td> \n",
       "        <td id=\"T_73da9efa_584a_11e9_81fa_701ce71031efrow4_col1\" class=\"data row4 col1\" >9909.45</td> \n",
       "        <td id=\"T_73da9efa_584a_11e9_81fa_701ce71031efrow4_col2\" class=\"data row4 col2\" >9000</td> \n",
       "        <td id=\"T_73da9efa_584a_11e9_81fa_701ce71031efrow4_col3\" class=\"data row4 col3\" >2089</td> \n",
       "        <td id=\"T_73da9efa_584a_11e9_81fa_701ce71031efrow4_col4\" class=\"data row4 col4\" >36.12%</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_73da9efa_584a_11e9_81fa_701ce71031eflevel0_row5\" class=\"row_heading level0 row5\" >2</th> \n",
       "        <td id=\"T_73da9efa_584a_11e9_81fa_701ce71031efrow5_col0\" class=\"data row5 col0\" >high_school</td> \n",
       "        <td id=\"T_73da9efa_584a_11e9_81fa_701ce71031efrow5_col1\" class=\"data row5 col1\" >8893.94</td> \n",
       "        <td id=\"T_73da9efa_584a_11e9_81fa_701ce71031efrow5_col2\" class=\"data row5 col2\" >8250</td> \n",
       "        <td id=\"T_73da9efa_584a_11e9_81fa_701ce71031efrow5_col3\" class=\"data row5 col3\" >22</td> \n",
       "        <td id=\"T_73da9efa_584a_11e9_81fa_701ce71031efrow5_col4\" class=\"data row5 col4\" >0.38%</td> \n",
       "    </tr></tbody> \n",
       "</table> "
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x17b2df65710>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_edu=get_sub_stats(data,'edu_')\n",
    "data_edu.style.format({\"percentage\":\"{:.2%}\"})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "salary_phd=data[data.edu_phd==1].monthly_salary * 10000\n",
    "salary_master=data[data.edu_master==1].monthly_salary * 10000\n",
    "salary_bachelor=data[data.edu_bachelor==1].monthly_salary * 10000\n",
    "salary_associate=data[data.edu_associate==1].monthly_salary * 10000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "大专，本科，硕士，博士的平均工资分别是9909，12803，17569，23750。中位数分别是9000， 12000，17500， 22500。\n"
     ]
    }
   ],
   "source": [
    "print('大专，本科，硕士，博士的平均工资分别是{:.0f}，{:.0f}，{:.0f}，{:.0f}。中位数分别是{:.0f}， {:.0f}，{:.0f}， {:.0f}。'.format(\n",
    "    salary_associate.mean(),salary_bachelor.mean(),salary_master.mean(),salary_phd.mean(),\n",
    "    salary_associate.median(),salary_bachelor.median(),salary_master.median(),salary_phd.median()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "F_onewayResult(statistic=4.602284295431452, pvalue=0.03314107457705273)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stats.f_oneway(salary_phd, salary_master)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "F_onewayResult(statistic=111.23781692887418, pvalue=1.2192245184207833e-25)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stats.f_oneway(salary_master, salary_bachelor)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "F_onewayResult(statistic=352.50570349900823, pvalue=2.660907641064028e-76)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stats.f_oneway(salary_bachelor, salary_associate)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "经过Oneway Anova Test，证明从大专到博士，学历每提高一级，工资都有显著的提高。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data=add_category_column(data,'edu')\n",
    "order=['phd','master','bachelor','associate']\n",
    "a=sns.boxplot(y='edu',x='monthly_salary',order=order,data=data[data.edu.isin(order)], orient='h')\n",
    "plt.annotate('https://github.com/juwikuang/job_survey', xy=(0.5,1.5), xytext=(2, 1.55))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 工作经验 Working Experience"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style  type=\"text/css\" >\n",
       "</style>  \n",
       "<table id=\"T_7aca731c_584a_11e9_b0f3_701ce71031ef\" > \n",
       "<thead>    <tr> \n",
       "        <th class=\"blank level0\" ></th> \n",
       "        <th class=\"col_heading level0 col0\" >experience_</th> \n",
       "        <th class=\"col_heading level0 col1\" >salary_mean</th> \n",
       "        <th class=\"col_heading level0 col2\" >salary_median</th> \n",
       "        <th class=\"col_heading level0 col3\" >head_count</th> \n",
       "        <th class=\"col_heading level0 col4\" >percentage</th> \n",
       "    </tr></thead> \n",
       "<tbody>    <tr> \n",
       "        <th id=\"T_7aca731c_584a_11e9_b0f3_701ce71031eflevel0_row0\" class=\"row_heading level0 row0\" >0</th> \n",
       "        <td id=\"T_7aca731c_584a_11e9_b0f3_701ce71031efrow0_col0\" class=\"data row0 col0\" >10</td> \n",
       "        <td id=\"T_7aca731c_584a_11e9_b0f3_701ce71031efrow0_col1\" class=\"data row0 col1\" >36868.4</td> \n",
       "        <td id=\"T_7aca731c_584a_11e9_b0f3_701ce71031efrow0_col2\" class=\"data row0 col2\" >29000</td> \n",
       "        <td id=\"T_7aca731c_584a_11e9_b0f3_701ce71031efrow0_col3\" class=\"data row0 col3\" >19</td> \n",
       "        <td id=\"T_7aca731c_584a_11e9_b0f3_701ce71031efrow0_col4\" class=\"data row0 col4\" >0.27%</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_7aca731c_584a_11e9_b0f3_701ce71031eflevel0_row1\" class=\"row_heading level0 row1\" >3</th> \n",
       "        <td id=\"T_7aca731c_584a_11e9_b0f3_701ce71031efrow1_col0\" class=\"data row1 col0\" >5_10</td> \n",
       "        <td id=\"T_7aca731c_584a_11e9_b0f3_701ce71031efrow1_col1\" class=\"data row1 col1\" >17411</td> \n",
       "        <td id=\"T_7aca731c_584a_11e9_b0f3_701ce71031efrow1_col2\" class=\"data row1 col2\" >16000</td> \n",
       "        <td id=\"T_7aca731c_584a_11e9_b0f3_701ce71031efrow1_col3\" class=\"data row1 col3\" >817</td> \n",
       "        <td id=\"T_7aca731c_584a_11e9_b0f3_701ce71031efrow1_col4\" class=\"data row1 col4\" >11.62%</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_7aca731c_584a_11e9_b0f3_701ce71031eflevel0_row2\" class=\"row_heading level0 row2\" >2</th> \n",
       "        <td id=\"T_7aca731c_584a_11e9_b0f3_701ce71031efrow2_col0\" class=\"data row2 col0\" >3_5</td> \n",
       "        <td id=\"T_7aca731c_584a_11e9_b0f3_701ce71031efrow2_col1\" class=\"data row2 col1\" >12651.6</td> \n",
       "        <td id=\"T_7aca731c_584a_11e9_b0f3_701ce71031efrow2_col2\" class=\"data row2 col2\" >12000</td> \n",
       "        <td id=\"T_7aca731c_584a_11e9_b0f3_701ce71031efrow2_col3\" class=\"data row2 col3\" >2026</td> \n",
       "        <td id=\"T_7aca731c_584a_11e9_b0f3_701ce71031efrow2_col4\" class=\"data row2 col4\" >28.83%</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_7aca731c_584a_11e9_b0f3_701ce71031eflevel0_row3\" class=\"row_heading level0 row3\" >4</th> \n",
       "        <td id=\"T_7aca731c_584a_11e9_b0f3_701ce71031efrow3_col0\" class=\"data row3 col0\" >no</td> \n",
       "        <td id=\"T_7aca731c_584a_11e9_b0f3_701ce71031efrow3_col1\" class=\"data row3 col1\" >10786.2</td> \n",
       "        <td id=\"T_7aca731c_584a_11e9_b0f3_701ce71031efrow3_col2\" class=\"data row3 col2\" >10000</td> \n",
       "        <td id=\"T_7aca731c_584a_11e9_b0f3_701ce71031efrow3_col3\" class=\"data row3 col3\" >1874</td> \n",
       "        <td id=\"T_7aca731c_584a_11e9_b0f3_701ce71031efrow3_col4\" class=\"data row3 col4\" >26.66%</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_7aca731c_584a_11e9_b0f3_701ce71031eflevel0_row4\" class=\"row_heading level0 row4\" >1</th> \n",
       "        <td id=\"T_7aca731c_584a_11e9_b0f3_701ce71031efrow4_col0\" class=\"data row4 col0\" >1_3</td> \n",
       "        <td id=\"T_7aca731c_584a_11e9_b0f3_701ce71031efrow4_col1\" class=\"data row4 col1\" >9962.57</td> \n",
       "        <td id=\"T_7aca731c_584a_11e9_b0f3_701ce71031efrow4_col2\" class=\"data row4 col2\" >9000</td> \n",
       "        <td id=\"T_7aca731c_584a_11e9_b0f3_701ce71031efrow4_col3\" class=\"data row4 col3\" >2292</td> \n",
       "        <td id=\"T_7aca731c_584a_11e9_b0f3_701ce71031efrow4_col4\" class=\"data row4 col4\" >32.61%</td> \n",
       "    </tr></tbody> \n",
       "</table> "
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x17b2e0b9ef0>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_experience=get_sub_stats(data,'experience_')\n",
    "data_experience.style.format({\"percentage\":\"{:.2%}\"})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "salary_we_10=data[data.experience_10==1].monthly_salary * 10000\n",
    "salary_we_5_10=data[data.experience_5_10==1].monthly_salary * 10000\n",
    "salary_we_3_5=data[data.experience_3_5==1].monthly_salary * 10000\n",
    "salary_we_1_3=data[data.experience_1_3==1].monthly_salary * 10000\n",
    "salary_we_no=data[data.experience_no==1].monthly_salary * 10000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data=add_category_column(data,'experience')\n",
    "order=['10','5_10','3_5','1_3']\n",
    "sns.boxplot(y='experience',x='monthly_salary',order=order,data=data[data.experience.isin(order)], orient='h')\n",
    "plt.annotate('https://github.com/juwikuang/job_survey', xy=(1.5,1.5), xytext=(1.55, 1.55))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 公司 Company"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 公司性质 Company Type"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style  type=\"text/css\" >\n",
       "</style>  \n",
       "<table id=\"T_7ef8a5a6_584a_11e9_a6d0_701ce71031ef\" > \n",
       "<thead>    <tr> \n",
       "        <th class=\"blank level0\" ></th> \n",
       "        <th class=\"col_heading level0 col0\" >company_type_</th> \n",
       "        <th class=\"col_heading level0 col1\" >salary_mean</th> \n",
       "        <th class=\"col_heading level0 col2\" >salary_median</th> \n",
       "        <th class=\"col_heading level0 col3\" >head_count</th> \n",
       "        <th class=\"col_heading level0 col4\" >percentage</th> \n",
       "    </tr></thead> \n",
       "<tbody>    <tr> \n",
       "        <th id=\"T_7ef8a5a6_584a_11e9_a6d0_701ce71031eflevel0_row0\" class=\"row_heading level0 row0\" >2</th> \n",
       "        <td id=\"T_7ef8a5a6_584a_11e9_a6d0_701ce71031efrow0_col0\" class=\"data row0 col0\" >foreign_rep</td> \n",
       "        <td id=\"T_7ef8a5a6_584a_11e9_a6d0_701ce71031efrow0_col1\" class=\"data row0 col1\" >18375</td> \n",
       "        <td id=\"T_7ef8a5a6_584a_11e9_a6d0_701ce71031efrow0_col2\" class=\"data row0 col2\" >15000</td> \n",
       "        <td id=\"T_7ef8a5a6_584a_11e9_a6d0_701ce71031efrow0_col3\" class=\"data row0 col3\" >4</td> \n",
       "        <td id=\"T_7ef8a5a6_584a_11e9_a6d0_701ce71031efrow0_col4\" class=\"data row0 col4\" >0.06%</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_7ef8a5a6_584a_11e9_a6d0_701ce71031eflevel0_row1\" class=\"row_heading level0 row1\" >6</th> \n",
       "        <td id=\"T_7ef8a5a6_584a_11e9_a6d0_701ce71031efrow1_col0\" class=\"data row1 col0\" >public_institution</td> \n",
       "        <td id=\"T_7ef8a5a6_584a_11e9_a6d0_701ce71031efrow1_col1\" class=\"data row1 col1\" >16961</td> \n",
       "        <td id=\"T_7ef8a5a6_584a_11e9_a6d0_701ce71031efrow1_col2\" class=\"data row1 col2\" >15833</td> \n",
       "        <td id=\"T_7ef8a5a6_584a_11e9_a6d0_701ce71031efrow1_col3\" class=\"data row1 col3\" >51</td> \n",
       "        <td id=\"T_7ef8a5a6_584a_11e9_a6d0_701ce71031efrow1_col4\" class=\"data row1 col4\" >0.77%</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_7ef8a5a6_584a_11e9_a6d0_701ce71031eflevel0_row2\" class=\"row_heading level0 row2\" >9</th> \n",
       "        <td id=\"T_7ef8a5a6_584a_11e9_a6d0_701ce71031efrow2_col0\" class=\"data row2 col0\" >us_eu</td> \n",
       "        <td id=\"T_7ef8a5a6_584a_11e9_a6d0_701ce71031efrow2_col1\" class=\"data row2 col1\" >13838</td> \n",
       "        <td id=\"T_7ef8a5a6_584a_11e9_a6d0_701ce71031efrow2_col2\" class=\"data row2 col2\" >12500</td> \n",
       "        <td id=\"T_7ef8a5a6_584a_11e9_a6d0_701ce71031efrow2_col3\" class=\"data row2 col3\" >273</td> \n",
       "        <td id=\"T_7ef8a5a6_584a_11e9_a6d0_701ce71031efrow2_col4\" class=\"data row2 col4\" >4.14%</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_7ef8a5a6_584a_11e9_a6d0_701ce71031eflevel0_row3\" class=\"row_heading level0 row3\" >8</th> \n",
       "        <td id=\"T_7ef8a5a6_584a_11e9_a6d0_701ce71031efrow3_col0\" class=\"data row3 col0\" >state</td> \n",
       "        <td id=\"T_7ef8a5a6_584a_11e9_a6d0_701ce71031efrow3_col1\" class=\"data row3 col1\" >12475</td> \n",
       "        <td id=\"T_7ef8a5a6_584a_11e9_a6d0_701ce71031efrow3_col2\" class=\"data row3 col2\" >12000</td> \n",
       "        <td id=\"T_7ef8a5a6_584a_11e9_a6d0_701ce71031efrow3_col3\" class=\"data row3 col3\" >445</td> \n",
       "        <td id=\"T_7ef8a5a6_584a_11e9_a6d0_701ce71031efrow3_col4\" class=\"data row3 col4\" >6.75%</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_7ef8a5a6_584a_11e9_a6d0_701ce71031eflevel0_row4\" class=\"row_heading level0 row4\" >0</th> \n",
       "        <td id=\"T_7ef8a5a6_584a_11e9_a6d0_701ce71031efrow4_col0\" class=\"data row4 col0\" >foreign</td> \n",
       "        <td id=\"T_7ef8a5a6_584a_11e9_a6d0_701ce71031efrow4_col1\" class=\"data row4 col1\" >12308</td> \n",
       "        <td id=\"T_7ef8a5a6_584a_11e9_a6d0_701ce71031efrow4_col2\" class=\"data row4 col2\" >12500</td> \n",
       "        <td id=\"T_7ef8a5a6_584a_11e9_a6d0_701ce71031efrow4_col3\" class=\"data row4 col3\" >204</td> \n",
       "        <td id=\"T_7ef8a5a6_584a_11e9_a6d0_701ce71031efrow4_col4\" class=\"data row4 col4\" >3.09%</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_7ef8a5a6_584a_11e9_a6d0_701ce71031eflevel0_row5\" class=\"row_heading level0 row5\" >3</th> \n",
       "        <td id=\"T_7ef8a5a6_584a_11e9_a6d0_701ce71031efrow5_col0\" class=\"data row5 col0\" >listed</td> \n",
       "        <td id=\"T_7ef8a5a6_584a_11e9_a6d0_701ce71031efrow5_col1\" class=\"data row5 col1\" >11994</td> \n",
       "        <td id=\"T_7ef8a5a6_584a_11e9_a6d0_701ce71031efrow5_col2\" class=\"data row5 col2\" >11500</td> \n",
       "        <td id=\"T_7ef8a5a6_584a_11e9_a6d0_701ce71031efrow5_col3\" class=\"data row5 col3\" >558</td> \n",
       "        <td id=\"T_7ef8a5a6_584a_11e9_a6d0_701ce71031efrow5_col4\" class=\"data row5 col4\" >8.46%</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_7ef8a5a6_584a_11e9_a6d0_701ce71031eflevel0_row6\" class=\"row_heading level0 row6\" >5</th> \n",
       "        <td id=\"T_7ef8a5a6_584a_11e9_a6d0_701ce71031efrow6_col0\" class=\"data row6 col0\" >private</td> \n",
       "        <td id=\"T_7ef8a5a6_584a_11e9_a6d0_701ce71031efrow6_col1\" class=\"data row6 col1\" >11478</td> \n",
       "        <td id=\"T_7ef8a5a6_584a_11e9_a6d0_701ce71031efrow6_col2\" class=\"data row6 col2\" >10500</td> \n",
       "        <td id=\"T_7ef8a5a6_584a_11e9_a6d0_701ce71031efrow6_col3\" class=\"data row6 col3\" >4904</td> \n",
       "        <td id=\"T_7ef8a5a6_584a_11e9_a6d0_701ce71031efrow6_col4\" class=\"data row6 col4\" >74.34%</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_7ef8a5a6_584a_11e9_a6d0_701ce71031eflevel0_row7\" class=\"row_heading level0 row7\" >7</th> \n",
       "        <td id=\"T_7ef8a5a6_584a_11e9_a6d0_701ce71031efrow7_col0\" class=\"data row7 col0\" >startup</td> \n",
       "        <td id=\"T_7ef8a5a6_584a_11e9_a6d0_701ce71031efrow7_col1\" class=\"data row7 col1\" >10946</td> \n",
       "        <td id=\"T_7ef8a5a6_584a_11e9_a6d0_701ce71031efrow7_col2\" class=\"data row7 col2\" >9500</td> \n",
       "        <td id=\"T_7ef8a5a6_584a_11e9_a6d0_701ce71031efrow7_col3\" class=\"data row7 col3\" >146</td> \n",
       "        <td id=\"T_7ef8a5a6_584a_11e9_a6d0_701ce71031efrow7_col4\" class=\"data row7 col4\" >2.21%</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_7ef8a5a6_584a_11e9_a6d0_701ce71031eflevel0_row8\" class=\"row_heading level0 row8\" >4</th> \n",
       "        <td id=\"T_7ef8a5a6_584a_11e9_a6d0_701ce71031efrow8_col0\" class=\"data row8 col0\" >non_profit</td> \n",
       "        <td id=\"T_7ef8a5a6_584a_11e9_a6d0_701ce71031efrow8_col1\" class=\"data row8 col1\" >9569</td> \n",
       "        <td id=\"T_7ef8a5a6_584a_11e9_a6d0_701ce71031efrow8_col2\" class=\"data row8 col2\" >10417</td> \n",
       "        <td id=\"T_7ef8a5a6_584a_11e9_a6d0_701ce71031efrow8_col3\" class=\"data row8 col3\" >12</td> \n",
       "        <td id=\"T_7ef8a5a6_584a_11e9_a6d0_701ce71031efrow8_col4\" class=\"data row8 col4\" >0.18%</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_7ef8a5a6_584a_11e9_a6d0_701ce71031eflevel0_row9\" class=\"row_heading level0 row9\" >1</th> \n",
       "        <td id=\"T_7ef8a5a6_584a_11e9_a6d0_701ce71031efrow9_col0\" class=\"data row9 col0\" >foreign_gov</td> \n",
       "        <td id=\"T_7ef8a5a6_584a_11e9_a6d0_701ce71031efrow9_col1\" class=\"data row9 col1\" >nan</td> \n",
       "        <td id=\"T_7ef8a5a6_584a_11e9_a6d0_701ce71031efrow9_col2\" class=\"data row9 col2\" >nan</td> \n",
       "        <td id=\"T_7ef8a5a6_584a_11e9_a6d0_701ce71031efrow9_col3\" class=\"data row9 col3\" >0</td> \n",
       "        <td id=\"T_7ef8a5a6_584a_11e9_a6d0_701ce71031efrow9_col4\" class=\"data row9 col4\" >0.00%</td> \n",
       "    </tr></tbody> \n",
       "</table> "
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x17b2dfafa90>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_company_type=get_sub_stats(data,'company_type_')\n",
    "data_company_type.style.format({\"percentage\":\"{:.2%}\",\"salary_mean\":\"{:.0f}\",\"salary_median\":\"{:.0f}\"})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "us_eu是欧美外企，startup是创业公司，listed是上市公司，state是国企，private是私企，foreign是非欧美外企，其他不足1000个样本的不管了。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 公司规模 Company Size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style  type=\"text/css\" >\n",
       "</style>  \n",
       "<table id=\"T_7ff4e336_584a_11e9_97bf_701ce71031ef\" > \n",
       "<thead>    <tr> \n",
       "        <th class=\"blank level0\" ></th> \n",
       "        <th class=\"col_heading level0 col0\" >company_size_</th> \n",
       "        <th class=\"col_heading level0 col1\" >salary_mean</th> \n",
       "        <th class=\"col_heading level0 col2\" >salary_median</th> \n",
       "        <th class=\"col_heading level0 col3\" >head_count</th> \n",
       "        <th class=\"col_heading level0 col4\" >percentage</th> \n",
       "    </tr></thead> \n",
       "<tbody>    <tr> \n",
       "        <th id=\"T_7ff4e336_584a_11e9_97bf_701ce71031eflevel0_row0\" class=\"row_heading level0 row0\" >0</th> \n",
       "        <td id=\"T_7ff4e336_584a_11e9_97bf_701ce71031efrow0_col0\" class=\"data row0 col0\" >10000</td> \n",
       "        <td id=\"T_7ff4e336_584a_11e9_97bf_701ce71031efrow0_col1\" class=\"data row0 col1\" >15609</td> \n",
       "        <td id=\"T_7ff4e336_584a_11e9_97bf_701ce71031efrow0_col2\" class=\"data row0 col2\" >12500</td> \n",
       "        <td id=\"T_7ff4e336_584a_11e9_97bf_701ce71031efrow0_col3\" class=\"data row0 col3\" >230</td> \n",
       "        <td id=\"T_7ff4e336_584a_11e9_97bf_701ce71031efrow0_col4\" class=\"data row0 col4\" >3.28%</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_7ff4e336_584a_11e9_97bf_701ce71031eflevel0_row1\" class=\"row_heading level0 row1\" >1</th> \n",
       "        <td id=\"T_7ff4e336_584a_11e9_97bf_701ce71031efrow1_col0\" class=\"data row1 col0\" >1000_5000</td> \n",
       "        <td id=\"T_7ff4e336_584a_11e9_97bf_701ce71031efrow1_col1\" class=\"data row1 col1\" >13655</td> \n",
       "        <td id=\"T_7ff4e336_584a_11e9_97bf_701ce71031efrow1_col2\" class=\"data row1 col2\" >12500</td> \n",
       "        <td id=\"T_7ff4e336_584a_11e9_97bf_701ce71031efrow1_col3\" class=\"data row1 col3\" >754</td> \n",
       "        <td id=\"T_7ff4e336_584a_11e9_97bf_701ce71031efrow1_col4\" class=\"data row1 col4\" >10.76%</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_7ff4e336_584a_11e9_97bf_701ce71031eflevel0_row2\" class=\"row_heading level0 row2\" >5</th> \n",
       "        <td id=\"T_7ff4e336_584a_11e9_97bf_701ce71031efrow2_col0\" class=\"data row2 col0\" >500_1000</td> \n",
       "        <td id=\"T_7ff4e336_584a_11e9_97bf_701ce71031efrow2_col1\" class=\"data row2 col1\" >12752</td> \n",
       "        <td id=\"T_7ff4e336_584a_11e9_97bf_701ce71031efrow2_col2\" class=\"data row2 col2\" >12500</td> \n",
       "        <td id=\"T_7ff4e336_584a_11e9_97bf_701ce71031efrow2_col3\" class=\"data row2 col3\" >599</td> \n",
       "        <td id=\"T_7ff4e336_584a_11e9_97bf_701ce71031efrow2_col4\" class=\"data row2 col4\" >8.55%</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_7ff4e336_584a_11e9_97bf_701ce71031eflevel0_row3\" class=\"row_heading level0 row3\" >4</th> \n",
       "        <td id=\"T_7ff4e336_584a_11e9_97bf_701ce71031efrow3_col0\" class=\"data row3 col0\" >5000_10000</td> \n",
       "        <td id=\"T_7ff4e336_584a_11e9_97bf_701ce71031efrow3_col1\" class=\"data row3 col1\" >12251</td> \n",
       "        <td id=\"T_7ff4e336_584a_11e9_97bf_701ce71031efrow3_col2\" class=\"data row3 col2\" >12250</td> \n",
       "        <td id=\"T_7ff4e336_584a_11e9_97bf_701ce71031efrow3_col3\" class=\"data row3 col3\" >96</td> \n",
       "        <td id=\"T_7ff4e336_584a_11e9_97bf_701ce71031efrow3_col4\" class=\"data row3 col4\" >1.37%</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_7ff4e336_584a_11e9_97bf_701ce71031eflevel0_row4\" class=\"row_heading level0 row4\" >2</th> \n",
       "        <td id=\"T_7ff4e336_584a_11e9_97bf_701ce71031efrow4_col0\" class=\"data row4 col0\" >150_500</td> \n",
       "        <td id=\"T_7ff4e336_584a_11e9_97bf_701ce71031efrow4_col1\" class=\"data row4 col1\" >12053</td> \n",
       "        <td id=\"T_7ff4e336_584a_11e9_97bf_701ce71031efrow4_col2\" class=\"data row4 col2\" >11500</td> \n",
       "        <td id=\"T_7ff4e336_584a_11e9_97bf_701ce71031efrow4_col3\" class=\"data row4 col3\" >1773</td> \n",
       "        <td id=\"T_7ff4e336_584a_11e9_97bf_701ce71031efrow4_col4\" class=\"data row4 col4\" >25.31%</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_7ff4e336_584a_11e9_97bf_701ce71031eflevel0_row5\" class=\"row_heading level0 row5\" >6</th> \n",
       "        <td id=\"T_7ff4e336_584a_11e9_97bf_701ce71031efrow5_col0\" class=\"data row5 col0\" >50_150</td> \n",
       "        <td id=\"T_7ff4e336_584a_11e9_97bf_701ce71031efrow5_col1\" class=\"data row5 col1\" >11459</td> \n",
       "        <td id=\"T_7ff4e336_584a_11e9_97bf_701ce71031efrow5_col2\" class=\"data row5 col2\" >10500</td> \n",
       "        <td id=\"T_7ff4e336_584a_11e9_97bf_701ce71031efrow5_col3\" class=\"data row5 col3\" >2150</td> \n",
       "        <td id=\"T_7ff4e336_584a_11e9_97bf_701ce71031efrow5_col4\" class=\"data row5 col4\" >30.69%</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_7ff4e336_584a_11e9_97bf_701ce71031eflevel0_row6\" class=\"row_heading level0 row6\" >3</th> \n",
       "        <td id=\"T_7ff4e336_584a_11e9_97bf_701ce71031efrow6_col0\" class=\"data row6 col0\" >50</td> \n",
       "        <td id=\"T_7ff4e336_584a_11e9_97bf_701ce71031efrow6_col1\" class=\"data row6 col1\" >10470</td> \n",
       "        <td id=\"T_7ff4e336_584a_11e9_97bf_701ce71031efrow6_col2\" class=\"data row6 col2\" >9000</td> \n",
       "        <td id=\"T_7ff4e336_584a_11e9_97bf_701ce71031efrow6_col3\" class=\"data row6 col3\" >1404</td> \n",
       "        <td id=\"T_7ff4e336_584a_11e9_97bf_701ce71031efrow6_col4\" class=\"data row6 col4\" >20.04%</td> \n",
       "    </tr></tbody> \n",
       "</table> "
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x17b2db3ff60>"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_company_size=get_sub_stats(data,'company_size_')\n",
    "data_company_size.style.format({\"percentage\":\"{:.2%}\",\"salary_mean\":\"{:.0f}\",\"salary_median\":\"{:.0f}\"})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "公司越大，工资越高"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 行业 Industry"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style  type=\"text/css\" >\n",
       "</style>  \n",
       "<table id=\"T_80fd15d8_584a_11e9_be34_701ce71031ef\" > \n",
       "<thead>    <tr> \n",
       "        <th class=\"blank level0\" ></th> \n",
       "        <th class=\"col_heading level0 col0\" >industry_</th> \n",
       "        <th class=\"col_heading level0 col1\" >salary_mean</th> \n",
       "        <th class=\"col_heading level0 col2\" >salary_median</th> \n",
       "        <th class=\"col_heading level0 col3\" >head_count</th> \n",
       "        <th class=\"col_heading level0 col4\" >percentage</th> \n",
       "    </tr></thead> \n",
       "<tbody>    <tr> \n",
       "        <th id=\"T_80fd15d8_584a_11e9_be34_701ce71031eflevel0_row0\" class=\"row_heading level0 row0\" >6</th> \n",
       "        <td id=\"T_80fd15d8_584a_11e9_be34_701ce71031efrow0_col0\" class=\"data row0 col0\" >logistic</td> \n",
       "        <td id=\"T_80fd15d8_584a_11e9_be34_701ce71031efrow0_col1\" class=\"data row0 col1\" >12811</td> \n",
       "        <td id=\"T_80fd15d8_584a_11e9_be34_701ce71031efrow0_col2\" class=\"data row0 col2\" >12500</td> \n",
       "        <td id=\"T_80fd15d8_584a_11e9_be34_701ce71031efrow0_col3\" class=\"data row0 col3\" >170</td> \n",
       "        <td id=\"T_80fd15d8_584a_11e9_be34_701ce71031efrow0_col4\" class=\"data row0 col4\" >2.05%</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_80fd15d8_584a_11e9_be34_701ce71031eflevel0_row1\" class=\"row_heading level0 row1\" >2</th> \n",
       "        <td id=\"T_80fd15d8_584a_11e9_be34_701ce71031efrow1_col0\" class=\"data row1 col0\" >edu</td> \n",
       "        <td id=\"T_80fd15d8_584a_11e9_be34_701ce71031efrow1_col1\" class=\"data row1 col1\" >12726</td> \n",
       "        <td id=\"T_80fd15d8_584a_11e9_be34_701ce71031efrow1_col2\" class=\"data row1 col2\" >11500</td> \n",
       "        <td id=\"T_80fd15d8_584a_11e9_be34_701ce71031efrow1_col3\" class=\"data row1 col3\" >419</td> \n",
       "        <td id=\"T_80fd15d8_584a_11e9_be34_701ce71031efrow1_col4\" class=\"data row1 col4\" >5.05%</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_80fd15d8_584a_11e9_be34_701ce71031eflevel0_row2\" class=\"row_heading level0 row2\" >4</th> \n",
       "        <td id=\"T_80fd15d8_584a_11e9_be34_701ce71031efrow2_col0\" class=\"data row2 col0\" >finance</td> \n",
       "        <td id=\"T_80fd15d8_584a_11e9_be34_701ce71031efrow2_col1\" class=\"data row2 col1\" >12186</td> \n",
       "        <td id=\"T_80fd15d8_584a_11e9_be34_701ce71031efrow2_col2\" class=\"data row2 col2\" >11500</td> \n",
       "        <td id=\"T_80fd15d8_584a_11e9_be34_701ce71031efrow2_col3\" class=\"data row2 col3\" >291</td> \n",
       "        <td id=\"T_80fd15d8_584a_11e9_be34_701ce71031efrow2_col4\" class=\"data row2 col4\" >3.51%</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_80fd15d8_584a_11e9_be34_701ce71031eflevel0_row3\" class=\"row_heading level0 row3\" >1</th> \n",
       "        <td id=\"T_80fd15d8_584a_11e9_be34_701ce71031efrow3_col0\" class=\"data row3 col0\" >computer</td> \n",
       "        <td id=\"T_80fd15d8_584a_11e9_be34_701ce71031efrow3_col1\" class=\"data row3 col1\" >11942</td> \n",
       "        <td id=\"T_80fd15d8_584a_11e9_be34_701ce71031efrow3_col2\" class=\"data row3 col2\" >11500</td> \n",
       "        <td id=\"T_80fd15d8_584a_11e9_be34_701ce71031efrow3_col3\" class=\"data row3 col3\" >6219</td> \n",
       "        <td id=\"T_80fd15d8_584a_11e9_be34_701ce71031efrow3_col4\" class=\"data row3 col4\" >74.94%</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_80fd15d8_584a_11e9_be34_701ce71031eflevel0_row4\" class=\"row_heading level0 row4\" >5</th> \n",
       "        <td id=\"T_80fd15d8_584a_11e9_be34_701ce71031efrow4_col0\" class=\"data row4 col0\" >gov</td> \n",
       "        <td id=\"T_80fd15d8_584a_11e9_be34_701ce71031efrow4_col1\" class=\"data row4 col1\" >11845</td> \n",
       "        <td id=\"T_80fd15d8_584a_11e9_be34_701ce71031efrow4_col2\" class=\"data row4 col2\" >10500</td> \n",
       "        <td id=\"T_80fd15d8_584a_11e9_be34_701ce71031efrow4_col3\" class=\"data row4 col3\" >174</td> \n",
       "        <td id=\"T_80fd15d8_584a_11e9_be34_701ce71031efrow4_col4\" class=\"data row4 col4\" >2.10%</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_80fd15d8_584a_11e9_be34_701ce71031eflevel0_row5\" class=\"row_heading level0 row5\" >10</th> \n",
       "        <td id=\"T_80fd15d8_584a_11e9_be34_701ce71031efrow5_col0\" class=\"data row5 col0\" >trade</td> \n",
       "        <td id=\"T_80fd15d8_584a_11e9_be34_701ce71031efrow5_col1\" class=\"data row5 col1\" >11428</td> \n",
       "        <td id=\"T_80fd15d8_584a_11e9_be34_701ce71031efrow5_col2\" class=\"data row5 col2\" >10500</td> \n",
       "        <td id=\"T_80fd15d8_584a_11e9_be34_701ce71031efrow5_col3\" class=\"data row5 col3\" >371</td> \n",
       "        <td id=\"T_80fd15d8_584a_11e9_be34_701ce71031efrow5_col4\" class=\"data row5 col4\" >4.47%</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_80fd15d8_584a_11e9_be34_701ce71031eflevel0_row6\" class=\"row_heading level0 row6\" >9</th> \n",
       "        <td id=\"T_80fd15d8_584a_11e9_be34_701ce71031efrow6_col0\" class=\"data row6 col0\" >service</td> \n",
       "        <td id=\"T_80fd15d8_584a_11e9_be34_701ce71031efrow6_col1\" class=\"data row6 col1\" >11215</td> \n",
       "        <td id=\"T_80fd15d8_584a_11e9_be34_701ce71031efrow6_col2\" class=\"data row6 col2\" >11500</td> \n",
       "        <td id=\"T_80fd15d8_584a_11e9_be34_701ce71031efrow6_col3\" class=\"data row6 col3\" >95</td> \n",
       "        <td id=\"T_80fd15d8_584a_11e9_be34_701ce71031efrow6_col4\" class=\"data row6 col4\" >1.14%</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_80fd15d8_584a_11e9_be34_701ce71031eflevel0_row7\" class=\"row_heading level0 row7\" >0</th> \n",
       "        <td id=\"T_80fd15d8_584a_11e9_be34_701ce71031efrow7_col0\" class=\"data row7 col0\" >ads</td> \n",
       "        <td id=\"T_80fd15d8_584a_11e9_be34_701ce71031efrow7_col1\" class=\"data row7 col1\" >10511</td> \n",
       "        <td id=\"T_80fd15d8_584a_11e9_be34_701ce71031efrow7_col2\" class=\"data row7 col2\" >9000</td> \n",
       "        <td id=\"T_80fd15d8_584a_11e9_be34_701ce71031efrow7_col3\" class=\"data row7 col3\" >117</td> \n",
       "        <td id=\"T_80fd15d8_584a_11e9_be34_701ce71031efrow7_col4\" class=\"data row7 col4\" >1.41%</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_80fd15d8_584a_11e9_be34_701ce71031eflevel0_row8\" class=\"row_heading level0 row8\" >8</th> \n",
       "        <td id=\"T_80fd15d8_584a_11e9_be34_701ce71031efrow8_col0\" class=\"data row8 col0\" >realestate</td> \n",
       "        <td id=\"T_80fd15d8_584a_11e9_be34_701ce71031efrow8_col1\" class=\"data row8 col1\" >10495</td> \n",
       "        <td id=\"T_80fd15d8_584a_11e9_be34_701ce71031efrow8_col2\" class=\"data row8 col2\" >10000</td> \n",
       "        <td id=\"T_80fd15d8_584a_11e9_be34_701ce71031efrow8_col3\" class=\"data row8 col3\" >147</td> \n",
       "        <td id=\"T_80fd15d8_584a_11e9_be34_701ce71031efrow8_col4\" class=\"data row8 col4\" >1.77%</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_80fd15d8_584a_11e9_be34_701ce71031eflevel0_row9\" class=\"row_heading level0 row9\" >7</th> \n",
       "        <td id=\"T_80fd15d8_584a_11e9_be34_701ce71031efrow9_col0\" class=\"data row9 col0\" >medical</td> \n",
       "        <td id=\"T_80fd15d8_584a_11e9_be34_701ce71031efrow9_col1\" class=\"data row9 col1\" >10412</td> \n",
       "        <td id=\"T_80fd15d8_584a_11e9_be34_701ce71031efrow9_col2\" class=\"data row9 col2\" >9000</td> \n",
       "        <td id=\"T_80fd15d8_584a_11e9_be34_701ce71031efrow9_col3\" class=\"data row9 col3\" >174</td> \n",
       "        <td id=\"T_80fd15d8_584a_11e9_be34_701ce71031efrow9_col4\" class=\"data row9 col4\" >2.10%</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_80fd15d8_584a_11e9_be34_701ce71031eflevel0_row10\" class=\"row_heading level0 row10\" >3</th> \n",
       "        <td id=\"T_80fd15d8_584a_11e9_be34_701ce71031efrow10_col0\" class=\"data row10 col0\" >energy</td> \n",
       "        <td id=\"T_80fd15d8_584a_11e9_be34_701ce71031efrow10_col1\" class=\"data row10 col1\" >10389</td> \n",
       "        <td id=\"T_80fd15d8_584a_11e9_be34_701ce71031efrow10_col2\" class=\"data row10 col2\" >9000</td> \n",
       "        <td id=\"T_80fd15d8_584a_11e9_be34_701ce71031efrow10_col3\" class=\"data row10 col3\" >122</td> \n",
       "        <td id=\"T_80fd15d8_584a_11e9_be34_701ce71031efrow10_col4\" class=\"data row10 col4\" >1.47%</td> \n",
       "    </tr></tbody> \n",
       "</table> "
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x17b2df55dd8>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_industry=get_sub_stats(data,'industry_')\n",
    "data_industry.style.format({\"percentage\":\"{:.2%}\",\"salary_mean\":\"{:.0f}\",\"salary_median\":\"{:.0f}\"})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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